Working as Assistant Professor in the Department of CSE-Cyber Security , utilizing my skills and abilities in Cyber security and Intelligence positions with over 14 years of experience . Skilled in Java,Python, Machine learning , Intrusion Detection System, Threat security, Cryptography & Information Security. Having knowledge of developing algorithms for data privacy, confidentiality and intrusion detection system, enhancing the security protocols and mitigating the risks of cyber threats, data analytics using packet capture tools like wireshark.
CAREER HIGHLIGHTS
• Expertise in Cyber security tools,kali linux, penetration and web vulnerability assessment.
• Expertize in application development, documentation and implementation of web applications
• Experience in Core Java, Swings, XML and SQL.
• Experience in C and C++ including OpenGL for graphics design.
• Handled multiple projects with various interdepartmental teams in data analytics using R, Project management using Jira
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Networks and Communications, Computer Science Applications, Artificial Intelligence, Software
8
Scopus Publications
15
Scholar Citations
2
Scholar h-index
Scopus Publications
Encrypted Vector Operations for Privacy-Preserving Machine Learning and Data Retrieval Deepthi V S, Sahana B, Mangala L, Venkat Chavan Nagabhushana International Conference on Emerging Technologies in Electronics and Green Energy Iceteg 2025, 2025 The growth of machine learning and advanced information retrieval raises serious privacy concerns when sensitive data is shared with untrusted parties. This work enables similar computations directly on encrypted data without revealing sensitive information. Partially Homomorphic Encryption with the Paillier cryptosystem is used to perform vector operations such as dot product, cosine similarity, and Euclidean distance securely in the encrypted domain. Experiments show near-plaintext accuracy while significantly reducing computational overhead compared to Fully Homomorphic Encryption. The results demonstrate that Partially Homomorphic Encryption provides a practical balance between security and efficiency, making it suitable for applications in healthcare, biometric verification, recommender systems, and federated learning.
SecureSmart: Blockchain-Assisted Adaptive Graph Neural Network for Intelligent Data Protection in Smart Cities Natrayan L, Deepthi V S, Saravanan B, Gayathri Naidu, Rajanish Kumar Kaushal, S.SriRagavi Proceedings of the 9th International Conference on Electronics Communication and Aerospace Technology Iceca 2025, 2025 The public, business, and academic sectors are becoming more interested in smart city infrastructure as a result of the Internet of Things (IoT) technologies quick development. Sensitivities related to vast amounts of data are naturally generated in smart city environments. Generating data and managing it accurately and securely are two of the biggest problems in smart cities. Growing data inconsistencies, cyberspace dangers, and human resources without appropriate encryption are all issues that smart cities continue to ignore, requiring smart and resilient solutions to new data risks and outdated procedures. A novel Adaptive Propagation Deep Graph Neural Network with Electric Eel Foraging Optimization (APDGNNet-EEFO) based on blockchain is proposed as a solution to this problem. Ensuring safe, accurate, and effective data sharing in smart cities is its primary goal. First, the raw data from a source is pre-processed using Reversible Automatic Selection Normalization (RASN). After that, the Polar Fox Optimization Algorithm (PFOA) is used for feature selection and extraction. APDGNNet uses the available data to detect potential attacks by utilizing Electric Eel Foraging Optimization (EEFO) to improve and correct the network weights. VOAEB is used to encrypt the remaining regular data after the conclusion of the detection step, and the encryption values are stored on the blockchain. The proposed APDGNNet-EEFO model achieved 99% overall accuracy, 98% precision, and 6 ms encryption and 7 ms decryption timings. Consequently, the proposed APDGNNet-EEFO model is useful and practicable.
Unmasking Manipulations: Recent Advances in Image Tampering Detection Chiyyedu Manasa, Dheeraj D, Deepthi V S 2nd IEEE International Conference on Iot Communication and Automation Technology Icicat 2024, 2024 This work aims at exploring the cutting-edge developments in image tampering detection, to unveil the latest techniques and methodologies employed to identify and expose digital image manipulations. The paper begins by reviewing the evolving landscape of image tampering and the growing need for advanced detection mechanisms in an era of sophisticated editing tools.The paper delves into pixel-based analysis, frequency-based analysis, statistical approaches, and the integration of deep learning in image tampering detection. Highlighting the strengths and limitations of each method, the review provides insights into how these approaches contribute to the ongoing battle against image forgeries.Additionally, the role of metadata examination, watermarking, and steganalysis is discussed in the context of enhancing the robustness of tampering detection. The paper addresses challenges posed by the rapid evolution of tampering techniques and the emergence of adversarial attacks, emphasizing the importance of adaptive and resilient detection strategies. Real-world applications of image tampering detection, ranging from criminal investigations to journalistic ethics, are explored to demonstrate the practical significance of the discussed advancements. The paper concludes by proposing avenues for future research, emphasizing the need for interdisciplinary collaboration and the development of standardized benchmarks to evaluate the effectiveness of emerging detection methods.
Image Copy Move Forgery Detection Using Multi-Plane Convolutional Neural Network S Vagdevi, Deepthi V S, Shweta Dhareshwar, Mohammed Al-Farouni, Hanumanthakari Kalyan Rao International Conference on Distributed Computing and Optimization Techniques Icdcot 2024, 2024 In digital images, the digital device usage and availability of open source for image editing applications are easily manipulating the digital images. The Copy Move Forgery (CMF) is an extensive technique for duplicate or hide particular image portion without leaving visual signs. Therefore, it is complex to detect the CMF and the forensic experts are rely on efficient technique for CMF detection. The Multi Plane Convolutional Neural Network (MP-CNN) is proposed in this research for detecting CMF in images. The MICC-F220, MICC-F2000 and CASIA 2.0 datasets are used which is publicly available Kaggle dataset. This dataset is preprocessed by image denoising which reduces the noise from input image. Then, the pre-processed images are provided to MP-CNN for detecting the CMF. The metrics such as precision, accuracy, f1score and recall are applied for estimating the MP-CNN performance. The MP-CNN attains accuracy of 99.45%, 99.26% and 98.61% for MICC-F220, MICC-F2000 and CASIA 2.0 datasets when compared to existing techniques like CNN, Masked Region CNN (M-RCNN) and Contrast Limited Adaptive Histogram Equalization with CNN (CLAHE+CNN).
Sine Cosine Reptile Search Algorithm with Grid Search Support Vector Machine based Ransomware Detection and Classification Myasar Mundher Adnan, S. Vaag Devi, Deepthi VS, Raghunathareddy MV, Nagendar Yamsani 2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024 The ransomware exploits the system data and takes off the significant information of the user without any intimation. Moreover, the ransomware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect complicated and packed malware. To overcome these problems, the Sine Cosine Reptile Search Algorithm with Grid Search Support Vector Machine (SCRSA-GSSVM) is proposed for ransomware detection and classification. The Drebin dataset is employed in this paper and min-max standardization is utilized aimed at preprocessing. The SCRSA is utilized for feature selection and GSSVM is utilized for classification. The SVM is tuned by GS which reduces the noise and false positives to enhance the model performance. Performance of SCRSA-GSSVM is assessed with presentation measure of accuracy, precision, recall and f1-score. The SCRSA-GSSVM attains 99.85% accuracy, 99.83% precision, 99.83% recall and 99.78% f1-score which is better when compared to Random Forest (RF) and Artificial Neural Network (ANN).
Statistical Analysis of Voice Based Emotion Recognition using Similarity Measures Chiyyedu Manasa, D. Dheeraj, V.S. Deepthi 1st International Conference on Advanced Technologies in Intelligent Control Environment Computing and Communication Engineering Icatiece 2019, 2019 Emotion recognition is gaining more and more importance. Emotional AI systems are allowed to detect, analyze, process and respond to people’s emotional states and moods. One specific application could include car navigation systems that are able to hear a driver start to experience road rage, and react to prevent them from making a rash driving decision. Another similar one could be used to allow automated assistants to change their approach when they hear anger or frustration from a user. Present voice assistance provides information to the user’s speech. This paper focuses on categorizing the emotions and applying statistical measures to identify the similarities among the different features. Preprocessing and Feature recognition is carried out using R statistical tool. The results are analyzed by identifying the similarities using Jaccard, Cosine and correlation similarity measures.
Behaviour analysis and detection of blackhole attacker node under reactive routing protocol in MANETs Deepthi V S, Vagdevi S 2018 International Conference on Networking Embedded and Wireless Systems Icnews 2018 Proceedings, 2018 Mobile Adhoc networks are wireless adhoc networks that have property of self organizing, less infrastructure, multi hoping, which are designed to work under low power vulnerable environment. Due to its very unique characteristics, there is much chances of threat of malicious nodes within the network. Blackhole attack is a menace in MANETs which redirects all traffic to itself and drops it. This paper’s objective is to analyze the effects of blackhole attack under reactive routing protocol such as Adhoc on Demand Distance Vector routing (AODV). The performance of this protocol is assessed to find the vulnerability of attack and also compared the impact of attack on both AODV, AODV with blackhole and proposed AODV protocols. The analysis is done by simulated using NS- 2.35 and QoS parameters such as Throughput, PDR, and Average Energy Consumed are measured further.
Multiphase Detection and Evaluation of AODV for Malicious Behaviour of a node in MANETs V S Deepthi, S Vagdevi 3rd International Conference on Electrical Electronics Communication Computer Technologies and Optimization Techniques Iceeccot 2018, 2018 Security threats are common in adhoc networks as there will be no fixed behaviour for node. The MANETs are adhoc networks which are liable to suffer from many such dangerous attacks. Wireless link for communication is used in which that makes network very vulnerable to an enemy's malicious behavior. The well known severity in malicious behaviour in ad-hoc networks is Black hole attack which exploits susceptibility of routing protocols like AODV. The proposed solution is to detect malicious node by checking the destination node sequence number with the next node seq number. Simulation is carried out in ns-2.35 which proves that the performance of network under malicious node behaviour and it has maximized by decreasing packet drop and also by detecting and avoiding black holes against the mobile adhoc network.
RECENT SCHOLAR PUBLICATIONS
SecureSmart: Blockchain-Assisted Adaptive Graph Neural Network for Intelligent Data Protection in Smart Cities L Natrayan, VS Deepthi, B Saravanan 2025 9th International Conference on Electronics, Communication and … , 2025 2025
Transparent Threat Detection Using SHAP and LIME to build an Explainable Intrusion Detection System DDVS Dharaneesh Kuruba, Gowtham R, Monish V, Vinayak Naik International Journal of Emerging Technologies and Innovative Research (www … , 2025 2025
Intelligent RAM Analysis Using ML for Detection and Prevention of Fileless Malware DDVS Meghana G Deshmukh, Mounika T, Parvati Waladunki, Puneetha G International Journal of Emerging Technologies and Innovative Research (www … , 2025 2025
Infrastructure-as-Code (IaC) Drift Security with GitOps-Driven Auto-Remediation in AWS DDVS Likitha Yogesh, Nidhi N, Spoorthi Rai ,Shreyas Reddy B International Journal of Emerging Technologies and Innovative Research (www … , 2025 2025
Encrypted Vector Operations for Privacy-Preserving Machine Learning and Data Retrieval VS Deepthi, B Sahana, L Mangala 2025 International Conference on Emerging Technologies in Electronics and … , 2025 2025
Unmasking manipulations: Recent advances in image tampering detection C Manasa, D Dheeraj, VS Deepthi 2024 International Conference on IoT, Communication and Automation … , 2024 2024 Citations: 1
Unveiling Human Intentions: EEGNET's Hybrid Role in Motion Detection DVS Dr. Deepthi V S Journal of Emerging Technologies and Innovative Research 6 (Issue 5), 1-5 , 2024 2024
Image Copy Move Forgery Detection Using Multi-Plane Convolutional Neural Network S Vagdevi, VS Deepthi, S Dhareshwar, M Al-Farouni, HK Rao 2024 International Conference on Distributed Computing and Optimization … , 2024 2024 Citations: 1
Sine Cosine Reptile Search Algorithm with Grid Search Support Vector Machine based Ransomware Detection and Classification SV Deepthi V S , Adnan, Myasar Mundher, N Yamsani 2024 International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 1
Movie Piracy Deterrence Using Infrared Transmitter and Steganography Technique DVS Priyasha M R, Madhav S, Chethan S, Mirza Meesum Raza Journal of emerging technologies and innovative research (JETIR) 10 (3), 96-102 , 2023 2023
Study on Crowd Density Estimation and Location Prediction in Public Transport System SS SSD, Deepthi VS, Venkat Chavan N International Journal of Innovative Research in Technology (IJIRT) 8 (10), 30-36 , 2022 2022
Crowd Density Estimation and Location Prediction in Public Transport System DVS VCN Sandhya Shanbhag, Sakshi S D International Journal of Engineering Research & Technology (IJERT) 11 (7), 68-71 , 2022 2022
APHEC: Attribute Policy Homomorphic Encryption and an Elliptic Curve Cryptography based Attack Classification Model for MANET Security VS Deepthi V S Design Engineering (Toronto) 2021 (Issue 9), 8955 - 8976 , 2021 2021
Transformation on The Dynamics of Indian Educational System Through The Ages MA UMAPATHY, P RAJAN, LDP KARPAGAVALLI, V DEEPTHI Design Engineering, 9600-9609 , 2021 2021
RCO based Multi Layer IDS in MANETs with Collective Fluctuation Measure VS Deepthi V S Design Engineering (Toronto) 2021 (Issue 08), 3976-3987 , 2021 2021
REVIEW ON DETECTION OF PHISHING ATTACKS USING MACHINE LEARNING ALGORITHM JMAPBBAS Prasada Journal of Emerging Technologies and Innovative Research 8 (5) , 2021 2021
REVIEW ON DETECTION OF SQLi and XSS ATTACKS AJ Varsha Venkatesh , B.U. Kavya , Sri Harsha A Journal of Emerging Technologies and Innovative Research 8 (5) , 2021 2021
IoT Based Smart Public Transport System with Fleet Analysis KB Deepthi V S, 2Dr. Vagdevi S, 3 Rasika R, 4Nidhi K Jain, 5Nipun Amar Journal of Emerging Technologies and Innovative Research 6 (Issue 5), 62-66 , 2019 2019
Performance analysis of AODV, OLSR, DSR, and DSDV Routing Protocols using NS3 Simulator VS Deepthi V S Journal of Emerging Technologies and Innovative Research (JETIR) 6 (Issue 5 … , 2019 2019
Statistical analysis of voice based emotion recognition using similarity measures C Manasa, D Dheeraj, VS Deepthi 2019 1st International Conference on Advanced Technologies in Intelligent … , 2019 2019 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
Statistical analysis of voice based emotion recognition using similarity measures C Manasa, D Dheeraj, VS Deepthi 2019 1st International Conference on Advanced Technologies in Intelligent … , 2019 2019 Citations: 7
Behaviour Analysis and Detection of Blackhole Attacker Node under Reactive Routing Protocol in MANETs VSD Vagdevi S 2018 International Conference on Networking, Embedded and Wireless Systems … , 2018 2018 Citations: 4
Unmasking manipulations: Recent advances in image tampering detection C Manasa, D Dheeraj, VS Deepthi 2024 International Conference on IoT, Communication and Automation … , 2024 2024 Citations: 1
Image Copy Move Forgery Detection Using Multi-Plane Convolutional Neural Network S Vagdevi, VS Deepthi, S Dhareshwar, M Al-Farouni, HK Rao 2024 International Conference on Distributed Computing and Optimization … , 2024 2024 Citations: 1
Sine Cosine Reptile Search Algorithm with Grid Search Support Vector Machine based Ransomware Detection and Classification SV Deepthi V S , Adnan, Myasar Mundher, N Yamsani 2024 International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 1
Multiphase Detection and Evaluation of AODV for Malicious Behaviour of a node in MANETs VS Deepthi, S Vagdevi 2018 International Conference on Electrical, Electronics, Communication … , 2018 2018 Citations: 1
SecureSmart: Blockchain-Assisted Adaptive Graph Neural Network for Intelligent Data Protection in Smart Cities L Natrayan, VS Deepthi, B Saravanan 2025 9th International Conference on Electronics, Communication and … , 2025 2025
Transparent Threat Detection Using SHAP and LIME to build an Explainable Intrusion Detection System DDVS Dharaneesh Kuruba, Gowtham R, Monish V, Vinayak Naik International Journal of Emerging Technologies and Innovative Research (www … , 2025 2025
Intelligent RAM Analysis Using ML for Detection and Prevention of Fileless Malware DDVS Meghana G Deshmukh, Mounika T, Parvati Waladunki, Puneetha G International Journal of Emerging Technologies and Innovative Research (www … , 2025 2025
Infrastructure-as-Code (IaC) Drift Security with GitOps-Driven Auto-Remediation in AWS DDVS Likitha Yogesh, Nidhi N, Spoorthi Rai ,Shreyas Reddy B International Journal of Emerging Technologies and Innovative Research (www … , 2025 2025
Encrypted Vector Operations for Privacy-Preserving Machine Learning and Data Retrieval VS Deepthi, B Sahana, L Mangala 2025 International Conference on Emerging Technologies in Electronics and … , 2025 2025
Unveiling Human Intentions: EEGNET's Hybrid Role in Motion Detection DVS Dr. Deepthi V S Journal of Emerging Technologies and Innovative Research 6 (Issue 5), 1-5 , 2024 2024
Movie Piracy Deterrence Using Infrared Transmitter and Steganography Technique DVS Priyasha M R, Madhav S, Chethan S, Mirza Meesum Raza Journal of emerging technologies and innovative research (JETIR) 10 (3), 96-102 , 2023 2023
Study on Crowd Density Estimation and Location Prediction in Public Transport System SS SSD, Deepthi VS, Venkat Chavan N International Journal of Innovative Research in Technology (IJIRT) 8 (10), 30-36 , 2022 2022
Crowd Density Estimation and Location Prediction in Public Transport System DVS VCN Sandhya Shanbhag, Sakshi S D International Journal of Engineering Research & Technology (IJERT) 11 (7), 68-71 , 2022 2022
APHEC: Attribute Policy Homomorphic Encryption and an Elliptic Curve Cryptography based Attack Classification Model for MANET Security VS Deepthi V S Design Engineering (Toronto) 2021 (Issue 9), 8955 - 8976 , 2021 2021
Transformation on The Dynamics of Indian Educational System Through The Ages MA UMAPATHY, P RAJAN, LDP KARPAGAVALLI, V DEEPTHI Design Engineering, 9600-9609 , 2021 2021
RCO based Multi Layer IDS in MANETs with Collective Fluctuation Measure VS Deepthi V S Design Engineering (Toronto) 2021 (Issue 08), 3976-3987 , 2021 2021
REVIEW ON DETECTION OF PHISHING ATTACKS USING MACHINE LEARNING ALGORITHM JMAPBBAS Prasada Journal of Emerging Technologies and Innovative Research 8 (5) , 2021 2021
REVIEW ON DETECTION OF SQLi and XSS ATTACKS AJ Varsha Venkatesh , B.U. Kavya , Sri Harsha A Journal of Emerging Technologies and Innovative Research 8 (5) , 2021 2021
